1. Field of the Invention
The present invention relates to a system and method of using multitask learning for improving a spoken dialog system.
2. Introduction
The present invention relates to a spoken dialog system with a particular application to a call routing system where the aim is to route the input calls in a customer care call center. In this spoken dialog system, callers are greeted by the open ended prompt “How May I Help You?” encouraging them to utter their requests in natural language. The system then tries to identify the customer's intent (call-type) using a natural language understanding component. The basic operation of a natural language understanding module within a spoken dialog system is know to those of skill in the art and is discussed more below with reference to FIG. 1. In the event the system is unable to understand the caller with high enough confidence, then the conversation will proceed with either a clarification or a confirmation prompt.
The understanding step and the process of a spoken dialog between a user and a computer system can be seen as a classification problem. For this purpose, data-driven classifiers are trained using large amounts of task data which is usually transcribed and then labeled by humans. This is an expensive and laborious process. The term “labeling” generally means assigning one or more of the predefined intents to each utterance. As an example, consider the utterance “I would like to know my account balance,” in a customer care application from a financial domain such as a bank. Assuming that the utterance is recognized correctly, the corresponding intent would be, for example, Request(Balance) and the action would be telling the balance to the user after prompting for the account number or routing this call to the billing department.
In previous work, a model adaptation approach has been used where a better model is built using the adaptation of an existing model of a similar application. See, G. Tur, “Model Adaptation for Spoken Language Understanding”, in Proceedings of the ICASSP, Philadelphia, Pa., May 2005, incorporated herein by reference. Furthermore, a library-based approach has been discussed where a human expert can bootstrap the new application model by manually selecting data from the library and augmenting them with rules. These approaches are expensive and take a long time to develop and train the models for intent classification. Therefore, what is needed in the art is an improved method of improving a spoken dialog system and generating and implementing intent classification in a natural language dialog system.